Overview

Dataset statistics

Number of variables15
Number of observations8626
Missing cells24692
Missing cells (%)19.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1011.0 KiB
Average record size in memory120.0 B

Variable types

Text2
Numeric7
Categorical6

Alerts

WEIGHT is highly overall correlated with HEIGHTHigh correlation
HEIGHT is highly overall correlated with WEIGHTHigh correlation
BPSYS is highly overall correlated with BPDIASHigh correlation
BPDIAS is highly overall correlated with BPSYS and 1 other fieldsHigh correlation
VISION is highly overall correlated with VISCORRHigh correlation
VISCORR is highly overall correlated with VISIONHigh correlation
HEARING is highly overall correlated with HEARAIDHigh correlation
HEARAID is highly overall correlated with HEARINGHigh correlation
HEARWAID is highly overall correlated with BPDIASHigh correlation
VISCORR is highly imbalanced (64.6%)Imbalance
VISWCORR is highly imbalanced (83.2%)Imbalance
HEARING is highly imbalanced (55.3%)Imbalance
HEARAID is highly imbalanced (65.3%)Imbalance
HEARWAID is highly imbalanced (62.2%)Imbalance
WEIGHT has 1003 (11.6%) missing valuesMissing
HEIGHT has 1763 (20.4%) missing valuesMissing
BPSYS has 1027 (11.9%) missing valuesMissing
BPDIAS has 1034 (12.0%) missing valuesMissing
HRATE has 1859 (21.6%) missing valuesMissing
VISION has 1848 (21.4%) missing valuesMissing
VISCORR has 1850 (21.4%) missing valuesMissing
VISWCORR has 3159 (36.6%) missing valuesMissing
HEARING has 1849 (21.4%) missing valuesMissing
HEARAID has 1858 (21.5%) missing valuesMissing
HEARWAID has 7442 (86.3%) missing valuesMissing
HEIGHT is highly skewed (γ1 = 47.37579693)Skewed
BPDIAS is highly skewed (γ1 = 24.44842815)Skewed
OASIS_session_label has unique valuesUnique
days_to_visit has 1355 (15.7%) zerosZeros

Reproduction

Analysis started2023-10-17 16:25:51.564281
Analysis finished2023-10-17 16:26:00.139233
Duration8.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct1378
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size67.5 KiB
2023-10-17T21:56:00.911142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters69008
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique131 ?
Unique (%)1.5%

Sample

1st rowOAS30001
2nd rowOAS30001
3rd rowOAS30001
4th rowOAS30001
5th rowOAS30001
ValueCountFrequency (%)
oas30446 32
 
0.4%
oas30936 31
 
0.4%
oas30675 30
 
0.3%
oas30393 30
 
0.3%
oas31155 28
 
0.3%
oas30194 28
 
0.3%
oas30314 26
 
0.3%
oas31160 25
 
0.3%
oas31100 25
 
0.3%
oas30825 24
 
0.3%
Other values (1368) 8347
96.8%
2023-10-17T21:56:01.252979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 11332
16.4%
0 9732
14.1%
O 8626
12.5%
A 8626
12.5%
S 8626
12.5%
1 4917
7.1%
2 2658
 
3.9%
7 2585
 
3.7%
4 2524
 
3.7%
5 2518
 
3.6%
Other values (3) 6864
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43130
62.5%
Uppercase Letter 25878
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 11332
26.3%
0 9732
22.6%
1 4917
11.4%
2 2658
 
6.2%
7 2585
 
6.0%
4 2524
 
5.9%
5 2518
 
5.8%
8 2345
 
5.4%
6 2315
 
5.4%
9 2204
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
O 8626
33.3%
A 8626
33.3%
S 8626
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 43130
62.5%
Latin 25878
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
3 11332
26.3%
0 9732
22.6%
1 4917
11.4%
2 2658
 
6.2%
7 2585
 
6.0%
4 2524
 
5.9%
5 2518
 
5.8%
8 2345
 
5.4%
6 2315
 
5.4%
9 2204
 
5.1%
Latin
ValueCountFrequency (%)
O 8626
33.3%
A 8626
33.3%
S 8626
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 11332
16.4%
0 9732
14.1%
O 8626
12.5%
A 8626
12.5%
S 8626
12.5%
1 4917
7.1%
2 2658
 
3.9%
7 2585
 
3.7%
4 2524
 
3.7%
5 2518
 
3.6%
Other values (3) 6864
9.9%

OASIS_session_label
Text

UNIQUE 

Distinct8626
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size67.5 KiB
2023-10-17T21:56:01.441629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length22
Median length20
Mean length20.003014
Min length20

Characters and Unicode

Total characters172546
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8626 ?
Unique (%)100.0%

Sample

1st rowOAS30001_UDSb1_d0000
2nd rowOAS30001_UDSb1_d0339
3rd rowOAS30001_UDSb1_d0722
4th rowOAS30001_UDSb1_d1106
5th rowOAS30001_UDSb1_d1456
ValueCountFrequency (%)
oas30001_udsb1_d0000 1
 
< 0.1%
oas30002_udsb1_d1850 1
 
< 0.1%
oas30001_udsb1_d1894 1
 
< 0.1%
oas30001_udsb1_d2181 1
 
< 0.1%
oas30001_udsb1_d2699 1
 
< 0.1%
oas30001_udsb1_d3025 1
 
< 0.1%
oas30001_udsb1_d3332 1
 
< 0.1%
oas30001_udsb1_d3675 1
 
< 0.1%
oas30001_udsb1_d3977 1
 
< 0.1%
oas30001_udsb1_d4349 1
 
< 0.1%
Other values (8616) 8616
99.9%
2023-10-17T21:56:01.724551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18864
10.9%
1 17853
10.3%
S 17252
 
10.0%
_ 17252
 
10.0%
3 14664
 
8.5%
O 8626
 
5.0%
A 8626
 
5.0%
d 8626
 
5.0%
b 8626
 
5.0%
D 8626
 
5.0%
Other values (9) 43531
25.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 86281
50.0%
Uppercase Letter 51756
30.0%
Connector Punctuation 17252
 
10.0%
Lowercase Letter 17252
 
10.0%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18864
21.9%
1 17853
20.7%
3 14664
17.0%
2 6035
 
7.0%
4 5575
 
6.5%
5 5025
 
5.8%
7 5003
 
5.8%
8 4591
 
5.3%
6 4470
 
5.2%
9 4201
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
S 17252
33.3%
O 8626
16.7%
A 8626
16.7%
D 8626
16.7%
U 8626
16.7%
Lowercase Letter
ValueCountFrequency (%)
d 8626
50.0%
b 8626
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 17252
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103538
60.0%
Latin 69008
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18864
18.2%
1 17853
17.2%
_ 17252
16.7%
3 14664
14.2%
2 6035
 
5.8%
4 5575
 
5.4%
5 5025
 
4.9%
7 5003
 
4.8%
8 4591
 
4.4%
6 4470
 
4.3%
Other values (2) 4206
 
4.1%
Latin
ValueCountFrequency (%)
S 17252
25.0%
O 8626
12.5%
A 8626
12.5%
d 8626
12.5%
b 8626
12.5%
D 8626
12.5%
U 8626
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 172546
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18864
10.9%
1 17853
10.3%
S 17252
 
10.0%
_ 17252
 
10.0%
3 14664
 
8.5%
O 8626
 
5.0%
A 8626
 
5.0%
d 8626
 
5.0%
b 8626
 
5.0%
D 8626
 
5.0%
Other values (9) 43531
25.2%

days_to_visit
Real number (ℝ)

ZEROS 

Distinct3426
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2031.2152
Minimum-39520
Maximum12334
Zeros1355
Zeros (%)15.7%
Negative5
Negative (%)0.1%
Memory size67.5 KiB
2023-10-17T21:56:01.857293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-39520
5-th percentile0
Q1469.75
median1510
Q33079
95-th percentile5619.25
Maximum12334
Range51854
Interquartile range (IQR)2609.25

Descriptive statistics

Standard deviation1951.0234
Coefficient of variation (CV)0.9605203
Kurtosis25.176248
Mean2031.2152
Median Absolute Deviation (MAD)1139
Skewness0.040577843
Sum17521262
Variance3806492.3
MonotonicityNot monotonic
2023-10-17T21:56:01.986740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1355
 
15.7%
371 26
 
0.3%
364 19
 
0.2%
385 18
 
0.2%
378 18
 
0.2%
357 14
 
0.2%
1099 14
 
0.2%
406 14
 
0.2%
350 13
 
0.2%
392 13
 
0.2%
Other values (3416) 7122
82.6%
ValueCountFrequency (%)
-39520 1
 
< 0.1%
-101 1
 
< 0.1%
-15 1
 
< 0.1%
-2 1
 
< 0.1%
-1 1
 
< 0.1%
0 1355
15.7%
1 5
 
0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
12334 1
< 0.1%
11849 1
< 0.1%
11723 1
< 0.1%
11639 1
< 0.1%
11504 1
< 0.1%
11493 1
< 0.1%
11303 1
< 0.1%
11066 1
< 0.1%
10928 1
< 0.1%
10711 1
< 0.1%

age at visit
Real number (ℝ)

Distinct3163
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.33844
Minimum-47.25
Maximum100.55
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size67.5 KiB
2023-10-17T21:56:02.107241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-47.25
5-th percentile59.41
Q169.27
median74.425
Q379.91
95-th percentile88.16
Maximum100.55
Range147.8
Interquartile range (IQR)10.64

Descriptive statistics

Standard deviation8.6251294
Coefficient of variation (CV)0.11602516
Kurtosis4.9007513
Mean74.33844
Median Absolute Deviation (MAD)5.305
Skewness-0.58549591
Sum641243.38
Variance74.392857
MonotonicityNot monotonic
2023-10-17T21:56:02.225534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.04 12
 
0.1%
70.75 12
 
0.1%
73.59 12
 
0.1%
73.78 11
 
0.1%
74.07 11
 
0.1%
68.75 11
 
0.1%
69.85 11
 
0.1%
75.18 11
 
0.1%
69.44 11
 
0.1%
67.53 11
 
0.1%
Other values (3153) 8513
98.7%
ValueCountFrequency (%)
-47.25 1
< 0.1%
42.5 1
< 0.1%
43.24 1
< 0.1%
43.5 1
< 0.1%
45.22 1
< 0.1%
45.24 1
< 0.1%
45.3 1
< 0.1%
45.52 1
< 0.1%
45.61 1
< 0.1%
45.66 2
< 0.1%
ValueCountFrequency (%)
100.55 1
< 0.1%
99.24 1
< 0.1%
98.95 1
< 0.1%
98.9 1
< 0.1%
98.73 1
< 0.1%
98.69 1
< 0.1%
98.34 1
< 0.1%
98.27 1
< 0.1%
97.98 1
< 0.1%
97.85 1
< 0.1%

WEIGHT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct652
Distinct (%)8.6%
Missing1003
Missing (%)11.6%
Infinite0
Infinite (%)0.0%
Mean174.42991
Minimum67
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.5 KiB
2023-10-17T21:56:02.397125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile114.05
Q1144
median167
Q3192
95-th percentile238
Maximum999
Range932
Interquartile range (IQR)48

Descriptive statistics

Standard deviation70.87728
Coefficient of variation (CV)0.40633673
Kurtosis86.749615
Mean174.42991
Median Absolute Deviation (MAD)24
Skewness8.0677249
Sum1329679.2
Variance5023.5889
MonotonicityNot monotonic
2023-10-17T21:56:02.637717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184 106
 
1.2%
160 104
 
1.2%
180 101
 
1.2%
156 100
 
1.2%
172 91
 
1.1%
162 86
 
1.0%
166 85
 
1.0%
154 82
 
1.0%
178 82
 
1.0%
187 81
 
0.9%
Other values (642) 6705
77.7%
(Missing) 1003
 
11.6%
ValueCountFrequency (%)
67 1
 
< 0.1%
74 2
< 0.1%
75.5 1
 
< 0.1%
78 2
< 0.1%
79 2
< 0.1%
81 2
< 0.1%
82 2
< 0.1%
83 1
 
< 0.1%
85 1
 
< 0.1%
88 3
< 0.1%
ValueCountFrequency (%)
999 26
0.3%
888 20
0.2%
326 1
 
< 0.1%
320 1
 
< 0.1%
317 1
 
< 0.1%
316 3
 
< 0.1%
315 1
 
< 0.1%
311 2
 
< 0.1%
310 1
 
< 0.1%
308 1
 
< 0.1%

HEIGHT
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct233
Distinct (%)3.4%
Missing1763
Missing (%)20.4%
Infinite0
Infinite (%)0.0%
Mean70.693765
Minimum51
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.5 KiB
2023-10-17T21:56:02.797562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile60
Q163
median65.5
Q369
95-th percentile72.5
Maximum9999
Range9948
Interquartile range (IQR)6

Descriptive statistics

Standard deviation208.30558
Coefficient of variation (CV)2.9465905
Kurtosis2255.2355
Mean70.693765
Median Absolute Deviation (MAD)2.8
Skewness47.375797
Sum485171.31
Variance43391.214
MonotonicityNot monotonic
2023-10-17T21:56:02.920517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 427
 
5.0%
62 396
 
4.6%
63 385
 
4.5%
67 384
 
4.5%
65 346
 
4.0%
70 335
 
3.9%
66 328
 
3.8%
69 279
 
3.2%
68 277
 
3.2%
61 217
 
2.5%
Other values (223) 3489
40.4%
(Missing) 1763
20.4%
ValueCountFrequency (%)
51 3
< 0.1%
52 4
< 0.1%
52.3 1
 
< 0.1%
52.4 1
 
< 0.1%
52.5 3
< 0.1%
52.7 1
 
< 0.1%
52.75 2
 
< 0.1%
53 6
0.1%
53.5 1
 
< 0.1%
54 4
< 0.1%
ValueCountFrequency (%)
9999 3
 
< 0.1%
999 2
 
< 0.1%
183 1
 
< 0.1%
99.9 10
 
0.1%
99 19
0.2%
88.8 37
0.4%
79.8 1
 
< 0.1%
79.5 1
 
< 0.1%
79.3 1
 
< 0.1%
79 7
 
0.1%

BPSYS
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct98
Distinct (%)1.3%
Missing1027
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean130.63416
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.5 KiB
2023-10-17T21:56:03.092975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile104
Q1118
median128
Q3140
95-th percentile160
Maximum999
Range997
Interquartile range (IQR)22

Descriptive statistics

Standard deviation41.580399
Coefficient of variation (CV)0.31829652
Kurtosis334.8999
Mean130.63416
Median Absolute Deviation (MAD)10
Skewness16.816224
Sum992689
Variance1728.9296
MonotonicityNot monotonic
2023-10-17T21:56:03.399165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 723
 
8.4%
130 646
 
7.5%
140 498
 
5.8%
110 458
 
5.3%
118 440
 
5.1%
122 410
 
4.8%
138 374
 
4.3%
128 360
 
4.2%
132 320
 
3.7%
124 299
 
3.5%
Other values (88) 3071
35.6%
(Missing) 1027
 
11.9%
ValueCountFrequency (%)
2 1
 
< 0.1%
85 1
 
< 0.1%
86 1
 
< 0.1%
88 4
 
< 0.1%
90 18
0.2%
92 5
 
0.1%
93 1
 
< 0.1%
94 9
0.1%
95 1
 
< 0.1%
96 13
0.2%
ValueCountFrequency (%)
999 10
0.1%
888 6
0.1%
408 1
 
< 0.1%
210 2
 
< 0.1%
202 1
 
< 0.1%
200 1
 
< 0.1%
198 2
 
< 0.1%
196 1
 
< 0.1%
194 3
 
< 0.1%
192 4
 
< 0.1%

BPDIAS
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct60
Distinct (%)0.8%
Missing1034
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean74.256718
Minimum40
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.5 KiB
2023-10-17T21:56:03.514741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile60
Q166
median72
Q380
95-th percentile90
Maximum999
Range959
Interquartile range (IQR)14

Descriptive statistics

Standard deviation34.928771
Coefficient of variation (CV)0.4703786
Kurtosis644.74787
Mean74.256718
Median Absolute Deviation (MAD)8
Skewness24.448428
Sum563757
Variance1220.0191
MonotonicityNot monotonic
2023-10-17T21:56:03.627983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 1132
13.1%
80 1049
12.2%
60 791
9.2%
78 577
 
6.7%
72 491
 
5.7%
82 425
 
4.9%
68 407
 
4.7%
64 407
 
4.7%
62 326
 
3.8%
84 251
 
2.9%
Other values (50) 1736
20.1%
(Missing) 1034
12.0%
ValueCountFrequency (%)
40 3
 
< 0.1%
42 2
 
< 0.1%
44 2
 
< 0.1%
46 1
 
< 0.1%
48 2
 
< 0.1%
50 54
0.6%
52 16
 
0.2%
53 1
 
< 0.1%
54 24
0.3%
55 7
 
0.1%
ValueCountFrequency (%)
999 10
 
0.1%
118 1
 
< 0.1%
112 1
 
< 0.1%
110 1
 
< 0.1%
108 1
 
< 0.1%
106 2
 
< 0.1%
104 4
 
< 0.1%
102 5
 
0.1%
100 48
0.6%
99 1
 
< 0.1%

HRATE
Real number (ℝ)

MISSING 

Distinct68
Distinct (%)1.0%
Missing1859
Missing (%)21.6%
Infinite0
Infinite (%)0.0%
Mean71.481159
Minimum40
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.5 KiB
2023-10-17T21:56:03.747888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile54
Q160
median68
Q376
95-th percentile88
Maximum999
Range959
Interquartile range (IQR)16

Descriptive statistics

Standard deviation51.703407
Coefficient of variation (CV)0.72331518
Kurtosis294.2878
Mean71.481159
Median Absolute Deviation (MAD)8
Skewness16.845219
Sum483713
Variance2673.2423
MonotonicityNot monotonic
2023-10-17T21:56:03.865950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 1117
12.9%
64 930
10.8%
72 837
9.7%
68 659
 
7.6%
80 571
 
6.6%
76 389
 
4.5%
56 328
 
3.8%
78 209
 
2.4%
84 185
 
2.1%
62 176
 
2.0%
Other values (58) 1366
15.8%
(Missing) 1859
21.6%
ValueCountFrequency (%)
40 5
 
0.1%
41 1
 
< 0.1%
42 7
 
0.1%
44 13
 
0.2%
45 2
 
< 0.1%
46 9
 
0.1%
48 71
0.8%
49 2
 
< 0.1%
50 37
0.4%
51 5
 
0.1%
ValueCountFrequency (%)
999 17
0.2%
888 4
 
< 0.1%
272 1
 
< 0.1%
156 1
 
< 0.1%
120 1
 
< 0.1%
118 1
 
< 0.1%
112 1
 
< 0.1%
110 2
 
< 0.1%
108 2
 
< 0.1%
104 7
0.1%

VISION
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)0.1%
Missing1848
Missing (%)21.4%
Memory size67.5 KiB
1.0
4538 
0.0
2106 
9.0
 
133
7.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters20334
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 4538
52.6%
0.0 2106
24.4%
9.0 133
 
1.5%
7.0 1
 
< 0.1%
(Missing) 1848
21.4%

Length

2023-10-17T21:56:03.984619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T21:56:04.117043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4538
67.0%
0.0 2106
31.1%
9.0 133
 
2.0%
7.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 8884
43.7%
. 6778
33.3%
1 4538
22.3%
9 133
 
0.7%
7 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13556
66.7%
Other Punctuation 6778
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8884
65.5%
1 4538
33.5%
9 133
 
1.0%
7 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 6778
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20334
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8884
43.7%
. 6778
33.3%
1 4538
22.3%
9 133
 
0.7%
7 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20334
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8884
43.7%
. 6778
33.3%
1 4538
22.3%
9 133
 
0.7%
7 1
 
< 0.1%

VISCORR
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)0.1%
Missing1850
Missing (%)21.4%
Memory size67.5 KiB
1.0
5524 
0.0
1237 
9.0
 
14
6.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters20328
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5524
64.0%
0.0 1237
 
14.3%
9.0 14
 
0.2%
6.0 1
 
< 0.1%
(Missing) 1850
 
21.4%

Length

2023-10-17T21:56:04.212220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T21:56:04.341373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5524
81.5%
0.0 1237
 
18.3%
9.0 14
 
0.2%
6.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 8013
39.4%
. 6776
33.3%
1 5524
27.2%
9 14
 
0.1%
6 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13552
66.7%
Other Punctuation 6776
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8013
59.1%
1 5524
40.8%
9 14
 
0.1%
6 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 6776
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20328
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8013
39.4%
. 6776
33.3%
1 5524
27.2%
9 14
 
0.1%
6 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8013
39.4%
. 6776
33.3%
1 5524
27.2%
9 14
 
0.1%
6 1
 
< 0.1%

VISWCORR
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)0.1%
Missing3159
Missing (%)36.6%
Memory size67.5 KiB
1.0
5188 
0.0
 
198
9.0
 
80
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters16401
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5188
60.1%
0.0 198
 
2.3%
9.0 80
 
0.9%
3.0 1
 
< 0.1%
(Missing) 3159
36.6%

Length

2023-10-17T21:56:04.450766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T21:56:04.562687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5188
94.9%
0.0 198
 
3.6%
9.0 80
 
1.5%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 5665
34.5%
. 5467
33.3%
1 5188
31.6%
9 80
 
0.5%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10934
66.7%
Other Punctuation 5467
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5665
51.8%
1 5188
47.4%
9 80
 
0.7%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 5467
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16401
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5665
34.5%
. 5467
33.3%
1 5188
31.6%
9 80
 
0.5%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16401
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5665
34.5%
. 5467
33.3%
1 5188
31.6%
9 80
 
0.5%
3 1
 
< 0.1%

HEARING
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)0.1%
Missing1849
Missing (%)21.4%
Memory size67.5 KiB
1.0
5429 
0.0
961 
9.0
 
386
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters20331
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5429
62.9%
0.0 961
 
11.1%
9.0 386
 
4.5%
3.0 1
 
< 0.1%
(Missing) 1849
 
21.4%

Length

2023-10-17T21:56:04.656661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T21:56:04.757139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5429
80.1%
0.0 961
 
14.2%
9.0 386
 
5.7%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 7738
38.1%
. 6777
33.3%
1 5429
26.7%
9 386
 
1.9%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13554
66.7%
Other Punctuation 6777
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7738
57.1%
1 5429
40.1%
9 386
 
2.8%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 6777
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20331
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7738
38.1%
. 6777
33.3%
1 5429
26.7%
9 386
 
1.9%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7738
38.1%
. 6777
33.3%
1 5429
26.7%
9 386
 
1.9%
3 1
 
< 0.1%

HEARAID
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)0.1%
Missing1858
Missing (%)21.5%
Memory size67.5 KiB
0.0
5577 
1.0
1171 
9.0
 
19
6.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters20304
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5577
64.7%
1.0 1171
 
13.6%
9.0 19
 
0.2%
6.0 1
 
< 0.1%
(Missing) 1858
 
21.5%

Length

2023-10-17T21:56:04.847123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T21:56:04.948961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5577
82.4%
1.0 1171
 
17.3%
9.0 19
 
0.3%
6.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 12345
60.8%
. 6768
33.3%
1 1171
 
5.8%
9 19
 
0.1%
6 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13536
66.7%
Other Punctuation 6768
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12345
91.2%
1 1171
 
8.7%
9 19
 
0.1%
6 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 6768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12345
60.8%
. 6768
33.3%
1 1171
 
5.8%
9 19
 
0.1%
6 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12345
60.8%
. 6768
33.3%
1 1171
 
5.8%
9 19
 
0.1%
6 1
 
< 0.1%

HEARWAID
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)0.3%
Missing7442
Missing (%)86.3%
Memory size67.5 KiB
1.0
1055 
0.0
 
82
9.0
 
47

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3552
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1055
 
12.2%
0.0 82
 
1.0%
9.0 47
 
0.5%
(Missing) 7442
86.3%

Length

2023-10-17T21:56:05.050075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-17T21:56:05.148372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1055
89.1%
0.0 82
 
6.9%
9.0 47
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 1266
35.6%
. 1184
33.3%
1 1055
29.7%
9 47
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2368
66.7%
Other Punctuation 1184
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1266
53.5%
1 1055
44.6%
9 47
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 1184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1266
35.6%
. 1184
33.3%
1 1055
29.7%
9 47
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1266
35.6%
. 1184
33.3%
1 1055
29.7%
9 47
 
1.3%

Interactions

2023-10-17T21:55:58.130432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:52.968039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:53.760053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:54.698726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:55.599243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:56.558634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:57.343762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:58.248325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:53.081725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:53.894361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:54.891172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:55.826539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:56.687598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:57.464748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:58.375358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:53.203991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:54.019365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:55.021586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:55.950114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:56.802934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:57.595979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:58.523307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:53.324654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:54.168638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:55.164174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:56.057927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:56.912082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:57.699363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:58.656353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:53.430140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:54.297382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:55.280715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:56.163233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:57.015275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:57.810956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:58.801452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:53.537421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:54.422463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:55.394350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:56.277496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:57.121235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:57.912796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:58.915047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:53.648620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:54.566560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:55.504516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:56.425370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:57.235302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-17T21:55:58.015894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-17T21:56:05.236139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
days_to_visitage at visitWEIGHTHEIGHTBPSYSBPDIASHRATEVISIONVISCORRVISWCORRHEARINGHEARAIDHEARWAID
days_to_visit1.0000.368-0.071-0.078-0.047-0.0920.0170.0510.0420.0000.0800.0750.000
age at visit0.3681.000-0.169-0.0830.117-0.161-0.0530.0250.0310.0380.1620.1560.086
WEIGHT-0.071-0.1691.0000.5490.1100.174-0.0220.0470.1190.0240.0480.1030.044
HEIGHT-0.078-0.0830.5491.000-0.0020.050-0.1060.0000.0000.0000.0000.0000.000
BPSYS-0.0470.1170.110-0.0021.0000.5050.0170.0560.1440.0000.0260.1240.015
BPDIAS-0.092-0.1610.1740.0500.5051.0000.0900.0760.2520.0000.0360.2151.000
HRATE0.017-0.053-0.022-0.1060.0170.0901.0000.0640.1470.0000.0090.1250.056
VISION0.0510.0250.0470.0000.0560.0760.0641.0000.6100.1060.1810.0670.102
VISCORR0.0420.0310.1190.0000.1440.2520.1470.6101.0000.1280.0510.2130.062
VISWCORR0.0000.0380.0240.0000.0000.0000.0000.1060.1281.0000.0350.0000.184
HEARING0.0800.1620.0480.0000.0260.0360.0090.1810.0510.0351.0000.7160.145
HEARAID0.0750.1560.1030.0000.1240.2150.1250.0670.2130.0000.7161.0000.241
HEARWAID0.0000.0860.0440.0000.0151.0000.0560.1020.0620.1840.1450.2411.000

Missing values

2023-10-17T21:55:59.114852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-17T21:55:59.454092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-17T21:55:59.997367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

OASISIDOASIS_session_labeldays_to_visitage at visitWEIGHTHEIGHTBPSYSBPDIASHRATEVISIONVISCORRVISWCORRHEARINGHEARAIDHEARWAID
0OAS30001OAS30001_UDSb1_d0000065.19999.000000999.0138.070.072.01.01.01.01.00.0NaN
1OAS30001OAS30001_UDSb1_d033933966.12155.00000064.0138.072.078.00.01.01.01.00.0NaN
2OAS30001OAS30001_UDSb1_d072272267.17162.00000064.0144.080.060.00.01.01.01.00.0NaN
3OAS30001OAS30001_UDSb1_d1106110668.22167.00000063.5130.082.068.00.01.01.01.00.0NaN
4OAS30001OAS30001_UDSb1_d1456145669.18173.00000063.5142.070.072.01.01.01.01.00.0NaN
5OAS30001OAS30001_UDSb1_d1894189470.38177.00000063.0126.076.068.00.01.01.01.00.0NaN
6OAS30001OAS30001_UDSb1_d2181218171.17180.00000063.0124.064.064.00.01.01.01.00.0NaN
7OAS30001OAS30001_UDSb1_d2699269972.59184.19995163.5140.078.064.01.01.01.01.00.0NaN
8OAS30001OAS30001_UDSb1_d3025302573.48180.00000063.5114.070.068.00.01.01.01.00.0NaN
9OAS30001OAS30001_UDSb1_d3332333274.32185.00000063.0164.090.058.01.01.01.01.00.0NaN
OASISIDOASIS_session_labeldays_to_visitage at visitWEIGHTHEIGHTBPSYSBPDIASHRATEVISIONVISCORRVISWCORRHEARINGHEARAIDHEARWAID
8616OAS31470OAS31470_UDSb1_d041941966.06NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8617OAS31471OAS31471_UDSb1_d0000065.30295.19995179.8132.078.054.01.01.01.01.00.0NaN
8618OAS31471OAS31471_UDSb1_d045745766.55NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8619OAS31472OAS31472_UDSb1_d0000067.27122.59997663.5100.060.060.01.01.01.01.00.0NaN
8620OAS31472OAS31472_UDSb1_d048248268.59NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8621OAS31472OAS31472_UDSb1_d082682669.53NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8622OAS31473OAS31473_UDSb1_d0000056.61219.00000076.5132.076.064.01.01.01.01.00.0NaN
8623OAS31473OAS31473_UDSb1_d1142114259.74NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
8624OAS31474OAS31474_UDSb1_d0000081.84151.00000063.0140.080.068.01.01.01.01.00.0NaN
8625OAS31474OAS31474_UDSb1_d073273283.85150.00000062.0138.062.060.01.01.01.01.00.0NaN